Pub Date : 2020-06-01DOI: 10.1109/SAM48682.2020.9104312
A. Koochakzadeh, P. Pal
This paper considers the problem of channel estimation for millimeter wave wireless communication channels. Many existing channel estimation approaches utilize the spatial sparsity of mmWave channels and employ compressive sensing based techniques to estimate the parameters of the channel, such as the Angles of Arrival (AoA) and Angles of Departure (AoD) of the channel paths. In this paper, we show how the problem of channel estimation can be converted into a fourth order tensor decomposition problem, which offers several benefits. Firstly, we do not need a grid-based search for the angles. More importantly, our algorithm is applicable for both uniform and non-uniform arrays at the transmitter and receiver. In particular, our method can exploit well-known benefits offered by the difference co-array of suitably designed sparse arrays and provably identify a larger number of channel paths compared to existing approaches1.
{"title":"Channel Estimation for Hybrid MIMO Communication with (Non-) Uniform Linear Arrays via Tensor Decomposition","authors":"A. Koochakzadeh, P. Pal","doi":"10.1109/SAM48682.2020.9104312","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104312","url":null,"abstract":"This paper considers the problem of channel estimation for millimeter wave wireless communication channels. Many existing channel estimation approaches utilize the spatial sparsity of mmWave channels and employ compressive sensing based techniques to estimate the parameters of the channel, such as the Angles of Arrival (AoA) and Angles of Departure (AoD) of the channel paths. In this paper, we show how the problem of channel estimation can be converted into a fourth order tensor decomposition problem, which offers several benefits. Firstly, we do not need a grid-based search for the angles. More importantly, our algorithm is applicable for both uniform and non-uniform arrays at the transmitter and receiver. In particular, our method can exploit well-known benefits offered by the difference co-array of suitably designed sparse arrays and provably identify a larger number of channel paths compared to existing approaches1.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"83 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80747713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SAM48682.2020.9104375
Yuchen Jiao, Yirong Ma, Yuantao Gu
Hyperspectral image clustering is an important and challenging problem, which aims to group image pixels according to the land cover information extracted from the spectrum. The spectrum observed at adjacent pixels are often highly-correlated, and leveraging such spatial correlation can greatly improve the clustering accuracy. Markov Random Field (MRF) is a powerful model to characterize such correlation. However, in this model the spatial parameter β often needs to be manually tuned, which brings difficulty in finding an optimal value. In this paper, we propose a novel hyperspectral clustering algorithm, which is able to learn parameter β from the data and thus achieves better performance. Specifically, we model the spectral information with Gaussian mixture model, and use variational expectation maximization method to complete the parameter estimation and clustering task. Experiments on both synthetic and real-world data sets verify the effectiveness of the proposed algorithm.
{"title":"Hyperspectral Image Clustering based on Variational Expectation Maximization","authors":"Yuchen Jiao, Yirong Ma, Yuantao Gu","doi":"10.1109/SAM48682.2020.9104375","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104375","url":null,"abstract":"Hyperspectral image clustering is an important and challenging problem, which aims to group image pixels according to the land cover information extracted from the spectrum. The spectrum observed at adjacent pixels are often highly-correlated, and leveraging such spatial correlation can greatly improve the clustering accuracy. Markov Random Field (MRF) is a powerful model to characterize such correlation. However, in this model the spatial parameter β often needs to be manually tuned, which brings difficulty in finding an optimal value. In this paper, we propose a novel hyperspectral clustering algorithm, which is able to learn parameter β from the data and thus achieves better performance. Specifically, we model the spectral information with Gaussian mixture model, and use variational expectation maximization method to complete the parameter estimation and clustering task. Experiments on both synthetic and real-world data sets verify the effectiveness of the proposed algorithm.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"102 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76105698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SAM48682.2020.9104223
Liangliang Li, Dan Luo, G. Bi, Xianpeng Wang, Dandan Meng
In this paper, a joint sparsity-inducing DOA estimation method is proposed for strictly noncircular sources with unknown mutual coupling. In the proposed method, two block-sparse recovery models are firstly formulated via parameterizing the steering vector without losing the array aperture. Then, taking the noncircularity of sources into account, a joint sparsity-inducing framework combined with reweighted l1 - norm optimization is constructed to estimate DOA, where the weighted matrix is structured by the noncircular MUSIC-like (NC MUSIC-like) spectrum function to strengthen the sparsity. Finally, DOA estimation can be realized via screening the position of nonzero blocks of the recovered block sparse matrix. Some simulations are implemented to demonstrate that the proposed method shows the effectiveness and superiority with unknown mutual coupling.
{"title":"Joint sparsity-inducing DOA estimation for strictly noncircular sources with unknown mutual coupling","authors":"Liangliang Li, Dan Luo, G. Bi, Xianpeng Wang, Dandan Meng","doi":"10.1109/SAM48682.2020.9104223","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104223","url":null,"abstract":"In this paper, a joint sparsity-inducing DOA estimation method is proposed for strictly noncircular sources with unknown mutual coupling. In the proposed method, two block-sparse recovery models are firstly formulated via parameterizing the steering vector without losing the array aperture. Then, taking the noncircularity of sources into account, a joint sparsity-inducing framework combined with reweighted l1 - norm optimization is constructed to estimate DOA, where the weighted matrix is structured by the noncircular MUSIC-like (NC MUSIC-like) spectrum function to strengthen the sparsity. Finally, DOA estimation can be realized via screening the position of nonzero blocks of the recovered block sparse matrix. Some simulations are implemented to demonstrate that the proposed method shows the effectiveness and superiority with unknown mutual coupling.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"46 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81275043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SAM48682.2020.9104248
Bingfan Liu, Baixiao Chen, Minglei Yang, Hui Xu
In this paper, we proposed a direction of arrival (DOA) estimation method based on sparse Bayesian learning (SBL) and a dynamic transmitted waveform design method for colocated multiple-input multiple-output (MIMO) radar. First, the SBL DOA estimation method is introduced into the MIMO radar with arbitrary transmitted waveforms. Our theoretical derivation shows that the estimation error of the SBL method is related to the transmitted waveforms. Then, we minimize the estimation error to obtain an updated transmitted waveforms, which will be transmitted in the next pulse repetition period. Numerical simulations show that compared with traditional orthogonal waveforms, the optimized waveforms could achieve a lower Cramér-Rao bound (CRB) and smaller DOA estimation error using the SBL method.
{"title":"DOA estimation using sparse Bayesian learning for colocated MIMO radar with dynamic waveforms","authors":"Bingfan Liu, Baixiao Chen, Minglei Yang, Hui Xu","doi":"10.1109/SAM48682.2020.9104248","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104248","url":null,"abstract":"In this paper, we proposed a direction of arrival (DOA) estimation method based on sparse Bayesian learning (SBL) and a dynamic transmitted waveform design method for colocated multiple-input multiple-output (MIMO) radar. First, the SBL DOA estimation method is introduced into the MIMO radar with arbitrary transmitted waveforms. Our theoretical derivation shows that the estimation error of the SBL method is related to the transmitted waveforms. Then, we minimize the estimation error to obtain an updated transmitted waveforms, which will be transmitted in the next pulse repetition period. Numerical simulations show that compared with traditional orthogonal waveforms, the optimized waveforms could achieve a lower Cramér-Rao bound (CRB) and smaller DOA estimation error using the SBL method.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"14 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86202712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SAM48682.2020.9104362
C. Shi, Yijie Wang, Fei Wang, Jianjiang Zhou
In this paper, a low probability of intercept (LPI) performance optimization scheme for a joint radar-communications system (JRCS) is proposed, which is able to simultaneously estimate channel parameters from the target returns and decode the received communications signals. The primary objective is to improve the LPI performance of a JRCS by optimizing radar waveform design and communications power allocation while guaranteeing a predefined mutual information (MI) threshold for channel parameter estimation and a desired communications data rate (CDR) for data transmission, where both traditional isolated sub-band (TISB) and radar isolated sub-band (RISB) situations are discussed. Subsequently, the approach of Lagrange multipliers and the Karush-Kuhn-Tuckers (KKT) optimality conditions are derived to solve the resulting problems. Also, the successive interference cancellation (SIC) technique is employed to obtain the original communications signals free of any radar interference. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed scheme.
{"title":"LPI Performance Optimization Scheme for a Joint Radar-Communications System","authors":"C. Shi, Yijie Wang, Fei Wang, Jianjiang Zhou","doi":"10.1109/SAM48682.2020.9104362","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104362","url":null,"abstract":"In this paper, a low probability of intercept (LPI) performance optimization scheme for a joint radar-communications system (JRCS) is proposed, which is able to simultaneously estimate channel parameters from the target returns and decode the received communications signals. The primary objective is to improve the LPI performance of a JRCS by optimizing radar waveform design and communications power allocation while guaranteeing a predefined mutual information (MI) threshold for channel parameter estimation and a desired communications data rate (CDR) for data transmission, where both traditional isolated sub-band (TISB) and radar isolated sub-band (RISB) situations are discussed. Subsequently, the approach of Lagrange multipliers and the Karush-Kuhn-Tuckers (KKT) optimality conditions are derived to solve the resulting problems. Also, the successive interference cancellation (SIC) technique is employed to obtain the original communications signals free of any radar interference. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed scheme.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"66 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91393495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SAM48682.2020.9104310
Hua Chen, Yonghong Liu, Qing Wang, Wei Liu, Zongju Peng, Gang Wang
In this paper, a reduced-rank direction-of-arrival (DOA) estimation algorithm for incoherently distributed (ID) noncircular sources based on a uniform linear array (ULA) is proposed. First the noncircularity property of the signal is utilized to establish an extended generalized array manifold (GAM) model based on the first-order Taylor series approximation. Then, the central DOA of source signals is obtained based on the generalized shift invariance property of the array manifold and the reduced-rank principle. Compared with the algorithm without exploiting the noncircularity information, the proposed algorithm can achieve a higher accuracy and handle more sources. Simulation results are provided to demonstrate the performance of the proposed algorithm.
{"title":"A general ESPRIT method for noncircularity-based incoherently distributed sources","authors":"Hua Chen, Yonghong Liu, Qing Wang, Wei Liu, Zongju Peng, Gang Wang","doi":"10.1109/SAM48682.2020.9104310","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104310","url":null,"abstract":"In this paper, a reduced-rank direction-of-arrival (DOA) estimation algorithm for incoherently distributed (ID) noncircular sources based on a uniform linear array (ULA) is proposed. First the noncircularity property of the signal is utilized to establish an extended generalized array manifold (GAM) model based on the first-order Taylor series approximation. Then, the central DOA of source signals is obtained based on the generalized shift invariance property of the array manifold and the reduced-rank principle. Compared with the algorithm without exploiting the noncircularity information, the proposed algorithm can achieve a higher accuracy and handle more sources. Simulation results are provided to demonstrate the performance of the proposed algorithm.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"2 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74818499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SAM48682.2020.9104376
Yu Zheng, Muran Guo, Lutao Liu
As the coprime array develops, the coprime multiple-input multiple-output (MIMO) radar has been proposed to achieve a large array aperture. However, holes also exist in the sum-difference coarray of the coprime MIMO radar, thus making the lags out of continuous range unavailable for the subspace based direction of arrival (DOA) estimation algorithm. In this paper, a coarray interpolation algorithm is proposed for the coprime MIMO radar to improve the estimation performance. The interpolation is completed by solving a nuclear norm based optimization problem, where the Toeplitz structure of the interpolated covariance matrix is exploited to reduce the computational complexity. The lags that are not continuous are utilized by using the proposed algorithm. Thus, the number of degrees of freedom (DOFs) and the estimation accuracy are improved. Numerical simulations are designed to examine the corresponding estimation performance.
{"title":"DOA Estimation Using Coarray Interpolation Algorithm Via Nuclear Norm Optimization for Coprime MIMO Radar","authors":"Yu Zheng, Muran Guo, Lutao Liu","doi":"10.1109/SAM48682.2020.9104376","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104376","url":null,"abstract":"As the coprime array develops, the coprime multiple-input multiple-output (MIMO) radar has been proposed to achieve a large array aperture. However, holes also exist in the sum-difference coarray of the coprime MIMO radar, thus making the lags out of continuous range unavailable for the subspace based direction of arrival (DOA) estimation algorithm. In this paper, a coarray interpolation algorithm is proposed for the coprime MIMO radar to improve the estimation performance. The interpolation is completed by solving a nuclear norm based optimization problem, where the Toeplitz structure of the interpolated covariance matrix is exploited to reduce the computational complexity. The lags that are not continuous are utilized by using the proposed algorithm. Thus, the number of degrees of freedom (DOFs) and the estimation accuracy are improved. Numerical simulations are designed to examine the corresponding estimation performance.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"24 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76864254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SAM48682.2020.9104302
Yanning Shen
Canonical correlation analysis (CCA) is a well-documented subspace learning approach widely used to seek for hidden sources common to two or multiple datasets. CCA has been applied in various learning tasks, such as dimensionality reduction, blind source separation, classification, and data fusion. Specifically, CCA aims at finding the subspaces for multi-view datasets, such that the projections of the multiple views onto the sought subspace is maximally correlated. However, simple linear projections may not be able to exploit general nonlinear projections, which motivates the development of nonlinear CCA. However, both conventional CCA and its non-linear variants do not take into consideration the data privacy, which is crucial especially when coping with personal data. To address this limitation, the present paper studies differentially private (DP) scheme for nonlinear CCA with privacy guarantee. Numerical tests on real datasets are carried out to showcase the effectiveness of the proposed algorithms.
{"title":"Differentially Private Nonlinear Canonical Correlation Analysis","authors":"Yanning Shen","doi":"10.1109/SAM48682.2020.9104302","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104302","url":null,"abstract":"Canonical correlation analysis (CCA) is a well-documented subspace learning approach widely used to seek for hidden sources common to two or multiple datasets. CCA has been applied in various learning tasks, such as dimensionality reduction, blind source separation, classification, and data fusion. Specifically, CCA aims at finding the subspaces for multi-view datasets, such that the projections of the multiple views onto the sought subspace is maximally correlated. However, simple linear projections may not be able to exploit general nonlinear projections, which motivates the development of nonlinear CCA. However, both conventional CCA and its non-linear variants do not take into consideration the data privacy, which is crucial especially when coping with personal data. To address this limitation, the present paper studies differentially private (DP) scheme for nonlinear CCA with privacy guarantee. Numerical tests on real datasets are carried out to showcase the effectiveness of the proposed algorithms.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"21 6 Suppl 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78021203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/SAM48682.2020.9104300
Jun Tao, Fengzhong Qu, Hongta Zhang
For single-carrier underwater acoustic (UWA) communications, phase correction is critical to the symbol detection on the receiver side. Existing receiver schemes either run a phase- locked loop (PLL) in parallel with an equalizer or perform the phase correction at the output of an equalizer. Both parallel and serial phase correction methods suffer limitations in practical use though. In this work, we propose to introduce a carrier frequency offset (CFO) pre-compensation module for existing receivers, with the CFO estimated with an m-sequence. The so-obtained receiver scheme was tested by real data collected in an at-sea UWA communication trial. Experimental results verified the extra performance gain brought by the CFO precompensation. In particular, when the CFO is the main source of phase rotation, conventional CFO correction modules like the PLL can be dropped without performance degradation.
{"title":"Direct Adaptive Equalization with CFO Pre-compensation for Single-Carrier Underwater Acoustic Communications","authors":"Jun Tao, Fengzhong Qu, Hongta Zhang","doi":"10.1109/SAM48682.2020.9104300","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104300","url":null,"abstract":"For single-carrier underwater acoustic (UWA) communications, phase correction is critical to the symbol detection on the receiver side. Existing receiver schemes either run a phase- locked loop (PLL) in parallel with an equalizer or perform the phase correction at the output of an equalizer. Both parallel and serial phase correction methods suffer limitations in practical use though. In this work, we propose to introduce a carrier frequency offset (CFO) pre-compensation module for existing receivers, with the CFO estimated with an m-sequence. The so-obtained receiver scheme was tested by real data collected in an at-sea UWA communication trial. Experimental results verified the extra performance gain brought by the CFO precompensation. In particular, when the CFO is the main source of phase rotation, conventional CFO correction modules like the PLL can be dropped without performance degradation.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"33 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74744197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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